论文标题
MULTICQA：零拍摄的自我监督文本匹配模型的零射击转移
MultiCQA: Zero-Shot Transfer of Self-Supervised Text Matching Models on a Massive Scale
论文作者
论文摘要
我们通过对社区问题回答论坛的140个来源域的自学培训来研究大规模的文本匹配模型的零射击传输能力。我们在答案选择和问题相似任务的九个基准上研究了模型性能，并表明所有140个模型都出奇地传递了出人意料，其中绝大多数模型基本上都优于常见的IR基准。我们还证明，考虑广泛选择的源域对于获得最佳的零拍传输性能至关重要，这与仅依赖最大和最相似域的标准程序对比。此外，我们广泛研究了如何最好地结合多个源域。我们建议在所有可用的源域中将自我监督与监督的多任务学习结合在一起。我们最佳的零射传输模型在六个基准测试上均优于内域的伯特和先前的最新技术。通过内域数据对我们的模型进行微调可带来更多的巨大收益，并在所有九个基准测试中实现了新的最新状态。
We study the zero-shot transfer capabilities of text matching models on a massive scale, by self-supervised training on 140 source domains from community question answering forums in English. We investigate the model performances on nine benchmarks of answer selection and question similarity tasks, and show that all 140 models transfer surprisingly well, where the large majority of models substantially outperforms common IR baselines. We also demonstrate that considering a broad selection of source domains is crucial for obtaining the best zero-shot transfer performances, which contrasts the standard procedure that merely relies on the largest and most similar domains. In addition, we extensively study how to best combine multiple source domains. We propose to incorporate self-supervised with supervised multi-task learning on all available source domains. Our best zero-shot transfer model considerably outperforms in-domain BERT and the previous state of the art on six benchmarks. Fine-tuning of our model with in-domain data results in additional large gains and achieves the new state of the art on all nine benchmarks.
